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VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections
March 26, 2024, 4:41 a.m. | Dongqi Fu, Zhigang Hua, Yan Xie, Jin Fang, Si Zhang, Kaan Sancak, Hao Wu, Andrey Malevich, Jingrui He, Bo Long
cs.LG updates on arXiv.org arxiv.org
Abstract: Graph transformer has been proven as an effective graph learning method for its adoption of attention mechanism that is capable of capturing expressive representations from complex topological and feature information of graphs. Graph transformer conventionally performs dense attention (or global attention) for every pair of nodes to learn node representation vectors, resulting in quadratic computational costs that are unaffordable for large-scale graph data. Therefore, mini-batch training for graph transformers is a promising direction, but limited …
abstract adoption arxiv attention cs.lg every feature global global attention graph graph learning graphs information nodes transformer type via virtual
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